Spiking Neural Networks: where neuroscience meets artificial intelligence

High energy consumption and the increasing computational cost of Artificial Neural Network (ANN) training 1 tend to be prohibitive. Furthermore, their difficulty and inability to learn even simple temporal tasks seem to trouble the research community. Nonetheless, one can observe natural intelligence with minuscule energy consumption, capable of creativity, problem-solving, and multitasking. Biological systems seem to have mastered information processing and response through natural evolution. The need to understand what makes them so effective and adapt these findings led to Spiking Neural Networks (SNNs). In this article, we will cover both the theory and a simplistic implementation of SNNs in PyTorch. Biological neuron cells do not behave like the neuron we use in ANNs. But what is it that makes them different?